Contribution Degree’s Application in the Research of Elements of TCM Syndromes

Abstract

AbstractUsing unsupervised algorithms to cluster for diagnosis information data is a mainstream and difficult area of TCM clinical research, and the optimal symptoms’ number of the syndrome is even more difficult to gain. However, there is no relevant and effective research on it yet. An unsupervised clustering algorithm is proposed based on the concepts of complex system entropy and contribution degree in this work. The algorithm is based on the familiar unsupervised complex system entropy cluster algorithm, simultaneously, it introduces contribution degree to self-adaptively select the symptoms’ number. This work carried out three clinical epidemiology surveys about depression, chronic renal failure and chronic hepatitis b, and obtained 1787 cases, each of which has measurements for 76 symptoms. The algorithm discovers 9 patterns, and 6 of them fit the syndrome in clinic. Therefore, we conclude that the algorithm provides an effective solution to discover syndrome from symptoms.Keywordscontribution degreemutual informationunsupervised clustersyndromeself-adaptive